Location Prediction with Personalized Federated Learning

Location Prediction with Personalized Federated Learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Location Prediction with Personalized Federated Learning shuang wang, Bowei Wang, Shuai Yao, Jiangqin Qu, Yuezheng Pan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1016044/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Location prediction has attracted wide attention in human mobility prediction because of the popularity of location-based social networks. Existing location prediction methods have achieved remarkable development in centrally stored datasets. However, these datasets contain privacy data about user behaviors and may cause privacy issues. A location prediction method is proposed in our work to predict human movement behavior using federated learning techniques in which the data is stored in different clients and different clients cooperate to train to extract useful users’ behavior information and prevent the disclosure of privacy. Firstly, we put forward an innovative spatial-temporal location prediction framework(STLPF) for location prediction by integrating spatial-temporal information in local and global views on each client, and propose a new loss function to optimize the model. Secondly, we design a new personalized federated learning framework in which clients can cooperatively train their personalized models in the absence of a global model. Finally, the numerous experimental results on check-in datasets further show that our privacy-protected method is superior and more effective than various baseline approaches. Computer Architecture and Engineering Software Engineering Scientific Communication location prediction self-attention federated learning spatial-temporal data analysis Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Minor Revision 06 Feb, 2022 Reviews received at journal 10 Nov, 2021 Reviewers invited by journal 10 Nov, 2021 Editor assigned by journal 01 Nov, 2021 First submitted to journal 25 Oct, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1016044","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":62455530,"identity":"5a14552b-c7d4-4758-b0a3-f3ec5a85976f","order_by":0,"name":"shuang wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYLACxgYbBgZm5gYoN4EoLWlALYykaTkMJonTYnD87OGXP3ecj+ZvZ2xgLqg4zMDPnmPA8HMHHi1n8tKsec/czp1xGKhlxpnDDJI9bwwYe8/g1mJ2IMfMmLHtdm4DSAtv22EGgxs5BsyMbXi0nH9jZviz7VzufLCWf4cZ7AlquZFj/IC37UDuBrAWYDgYSBDQYn/jjRnQPcm5G4FaDvMcS+eROPOs4GAvHi2S/TnGH3+22eXOO3/44GOeGms5/vbkjQ9+4tECBGwSMNYBIOaBMfAB5g8EFIyCUTAKRsFIBwB+ClSlEUC9VQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-1533-1051","institution":"Northeastern University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"shuang","middleName":"","lastName":"wang","suffix":""},{"id":62455531,"identity":"0a30b831-5390-4daf-9a3f-270ef73119dd","order_by":1,"name":"Bowei Wang","email":"","orcid":"","institution":"Northeastern University Software College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bowei","middleName":"","lastName":"Wang","suffix":""},{"id":62455532,"identity":"7ffc5927-8470-4356-9602-aa6dde51e0d9","order_by":2,"name":"Shuai Yao","email":"","orcid":"","institution":"Northeastern University Software College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Yao","suffix":""},{"id":62455533,"identity":"1cb352b2-8787-47c9-9fd2-c6ad77c3123f","order_by":3,"name":"Jiangqin Qu","email":"","orcid":"","institution":"Northeastern University Software College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiangqin","middleName":"","lastName":"Qu","suffix":""},{"id":62455534,"identity":"dfc918c0-d1d5-4906-b772-e32436fe3761","order_by":4,"name":"Yuezheng Pan","email":"","orcid":"","institution":"Northeastern University Software College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuezheng","middleName":"","lastName":"Pan","suffix":""}],"badges":[],"createdAt":"2021-10-25 11:34:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1016044/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1016044/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":15445329,"identity":"44835f3d-a6cb-47d2-bcfe-23c2ec8fbb8d","added_by":"auto","created_at":"2021-11-11 15:45:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":886815,"visible":true,"origin":"","legend":"","description":"","filename":"paper10.9.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1016044/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"Location Prediction with Personalized Federated Learning","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1016044/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"location prediction, self-attention, federated learning, spatial-temporal data analysis","lastPublishedDoi":"10.21203/rs.3.rs-1016044/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1016044/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLocation prediction has attracted wide attention in human mobility prediction because of the popularity of location-based social networks. Existing location prediction methods have achieved remarkable development in centrally stored datasets. However, these datasets contain privacy data about user behaviors and may cause privacy issues. A location prediction method is proposed in our work to predict human movement behavior using federated learning techniques in which the data is stored in different clients and different clients cooperate to train to extract useful users\u0026rsquo; behavior information and prevent the disclosure of privacy. Firstly, we put forward an innovative spatial-temporal location prediction framework(STLPF) for location prediction by integrating spatial-temporal information in local and global views on each client, and propose a new loss function to optimize the model. Secondly, we design a new personalized federated learning framework in which clients can cooperatively train their personalized models in the absence of a global model. Finally, the numerous experimental results on check-in datasets further show that our privacy-protected method is superior and more effective than various baseline approaches.\u003c/p\u003e","manuscriptTitle":"Location Prediction with Personalized Federated Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-11-11 15:44:55","doi":"10.21203/rs.3.rs-1016044/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor Revision","date":"2022-02-07T02:15:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-11-10T11:16:59+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-11-10T06:46:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-11-01T06:05:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Soft Computing","date":"2021-10-25T07:34:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d3735f0f-dfe8-47bc-bf64-5f6592efc744","owner":[],"postedDate":"November 11th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":8444762,"name":"Computer Architecture and Engineering"},{"id":8444763,"name":"Software Engineering"},{"id":8444764,"name":"Scientific Communication"}],"tags":[],"updatedAt":"2022-03-19T07:28:22+00:00","versionOfRecord":[],"versionCreatedAt":"2021-11-11 15:44:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1016044","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1016044","identity":"rs-1016044","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}